Abstract
Nowadays, target or object tracking has become an important issue for many areas especially for research in defense industry. Parameters such as the position, speed and direction of the target being followed, and the attack angle-velocity of the shooting system are important. The primary solutions for parameter estimations (information) such as speed and orientation in target tracking are alpha-beta and alpha-beta-gamma filters. These filters have attracted attention for a while due to their ease of use in applications and calculations. Since these non-adaptive structures cannot alter their parameters according to changing conditions, their filtering coefficients are fixed, and can be turned into adaptive-dynamic according to the need in problem. For this reason, Kalman-based filter approaches have emerged as an alternative solution in target tracking over time. In this study, the performances of the mentioned filter algorithms are compared through some case studies, and their superiority and differences from each other are emphasized according to the simulation results.
References
- Y. Kosuge, “New α-β filters in terms of steady state velocity,” in Proc. of the ICCAS-SICE, pp. 2674-2679, 2009.
- T. Lee, J. Su, K. Hsia, K. Yu, and C. Wang, “Design of an alpha-beta filter by combining fuzzy logic with evolutionary methods,” in Proc. of the 2010 Int. Symp. on Computer, Communication, Control and Automation (3CA), pp. 270-273, 2010.
- P.R. Kalata, “The Tracking Index: A Generalized Parameter for α-β and α-β-γ Target Trackers,” AESS, vol. 20, pp. 174-182, 1984.
- E. Üstüay, “Manevra yapan hedeflerin konum ve kinematik bilgilerini en iyi kestiren filtrelerin iyileştirilmesi ve yeni bir yaklaşım olan şablon filtresinin tasarımı,” M.S. thesis, Yıldız Teknik Üniversitesi, FBE, İstanbul, pp. 11-28, 2007.
- A. Lana, “Kalman filtresi ve olasılıksal veri ilişkilendirme yöntemlerini kullanan çoklu hedef izleme algoritmaları,” M.S. thesis, İstanbul Teknik Üniversitesi, FBE, İstanbul, pp. 21-37, 2001.
- I. Hwang and H. Balakrishnan, “Multiple-target tracking and identity management with application to aircraft tracking,” J. Guid. Control Dyn., vol. 30, no. 3, 2007.
- H. Masnadi-Shirazi, A. Masnadi-Shirazi, and M. A. Dastgheib, “A step by step mathematical derivation and tutorial on Kalman filters,” arXiv, Oct. 9, 2019.
- N.R. Nair, P. Sudheesh, and M. Jayakumar, “2-D airborne vehicle tracking using Kalman filter,” in Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT), 2016.
- L. Rakai, H. Song, S. Sun, W. Zhang, and Y. Yang, “Data association in multiple object tracking: A survey of recent techniques,” Expert Syst. Appl., vol. 192, 2022.
- E. Duymaz, A. E. Oğuz, and H. Temeltaş, “Exact flow of particles using for state estimations in unmanned aerial systems' navigation,” PLoS ONE, vol. 15, no. 4, 2020.
- E. Duymaz and B. Işık, “A novel method for tilt compensation in inertial sensor systems,” in SIU 2021, pp. 1-4, 2021.
- M.A. Murzova and V.E. Farber, “The α-β filter for tracking maneuvering objects with LFM waveforms,” in Proc. of the 2017 IV Int. Conf. on Engineering and Telecommunication (EnT), pp. 104-107, 2017.
- I. Ostroumov, V. Larin, Y. Averyanova, O. Sushchenko, M. Zaliskyi, and Y. Bezkorovainyi, “Performance analysis of alpha-beta-gamma filter for airplane tracking using automatic dependent surveillance-broadcast,” in Int. Workshop on Advances in Civil Aviation Systems Development, Cham: Springer Nature Switzerland, pp. 60-72, 2023.
- D. Tenne and T. Singh, “Optimal design of α-β-(γ) filters,” in Proc. of the American Control Conference, pp. 4348-4352, 2000.
- T. Jeong, A. W. Njonjo, and B.F. Pan, “A study on the performance comparison of three optimal Alpha-Beta-Gamma filters and Alpha-Beta-Gamma-Eta filter for a high dynamic target,” TransNav, vol. 11, pp. 1-10, 2017.
- B.I. Sighencea, R.I. Stanciu, C. Șorândaru, and C.D. Căleanu, “The Alpha-Beta family of filters to solve the threshold problem: A comparison,” Mathematics, vol. 10, no. 6, 2022.
- J. Khan and K. Kim, “A performance evaluation of the alpha-beta (α-β) filter algorithm with different learning models: DBN, DELM, and SVM,” Applied Sciences, vol. 12, no. 19, 2022.
Abstract
Günümüzde hedef takibi bir çok alanda özellikle savunma sanayi çalışmalarında önemli bir konu haline gelmiştir. Takip edilen hedefin konumu, hızı, yönü ile hedefe atış planlanıyor ise atış sisteminin hareket açısı ve hızı gibi parametreler önem arz eder. Hedef takibinde konum-hız, yönelim gibi bilgilerin kestiriminde en eski çözümler alfa-beta ve alfa-beta-gama filtreleridir. Bu filtreler uygulama ve hesaplama kolaylığı özellikleri sebebiyle bir süre ilgi görmüştür. Uyarlamalı olmayan bu yapılar, değişen koşullara göre parametrelerini değiştiremediklerinden filtreleme katsayıları sabittir, probleme göre uyarlamalı-dinamik hale getirilmeleri gerekebilir. Bu nedenle Kalman tabanlı filtre yaklaşımları hedef takibinde zamanla başka bir çözüm olarak ortaya çıkmıştır. Bu çalışmada, sözü geçen filtre algoritmaları örnek bir durum çalışması üzerinden performans karşılaştırılmasına tabi tutulmuş, benzetim sonuçlarına göre üstünlük ve birbirlerinden farklılıkları vurgulanmıştır.
References
- Y. Kosuge, “New α-β filters in terms of steady state velocity,” in Proc. of the ICCAS-SICE, pp. 2674-2679, 2009.
- T. Lee, J. Su, K. Hsia, K. Yu, and C. Wang, “Design of an alpha-beta filter by combining fuzzy logic with evolutionary methods,” in Proc. of the 2010 Int. Symp. on Computer, Communication, Control and Automation (3CA), pp. 270-273, 2010.
- P.R. Kalata, “The Tracking Index: A Generalized Parameter for α-β and α-β-γ Target Trackers,” AESS, vol. 20, pp. 174-182, 1984.
- E. Üstüay, “Manevra yapan hedeflerin konum ve kinematik bilgilerini en iyi kestiren filtrelerin iyileştirilmesi ve yeni bir yaklaşım olan şablon filtresinin tasarımı,” M.S. thesis, Yıldız Teknik Üniversitesi, FBE, İstanbul, pp. 11-28, 2007.
- A. Lana, “Kalman filtresi ve olasılıksal veri ilişkilendirme yöntemlerini kullanan çoklu hedef izleme algoritmaları,” M.S. thesis, İstanbul Teknik Üniversitesi, FBE, İstanbul, pp. 21-37, 2001.
- I. Hwang and H. Balakrishnan, “Multiple-target tracking and identity management with application to aircraft tracking,” J. Guid. Control Dyn., vol. 30, no. 3, 2007.
- H. Masnadi-Shirazi, A. Masnadi-Shirazi, and M. A. Dastgheib, “A step by step mathematical derivation and tutorial on Kalman filters,” arXiv, Oct. 9, 2019.
- N.R. Nair, P. Sudheesh, and M. Jayakumar, “2-D airborne vehicle tracking using Kalman filter,” in Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT), 2016.
- L. Rakai, H. Song, S. Sun, W. Zhang, and Y. Yang, “Data association in multiple object tracking: A survey of recent techniques,” Expert Syst. Appl., vol. 192, 2022.
- E. Duymaz, A. E. Oğuz, and H. Temeltaş, “Exact flow of particles using for state estimations in unmanned aerial systems' navigation,” PLoS ONE, vol. 15, no. 4, 2020.
- E. Duymaz and B. Işık, “A novel method for tilt compensation in inertial sensor systems,” in SIU 2021, pp. 1-4, 2021.
- M.A. Murzova and V.E. Farber, “The α-β filter for tracking maneuvering objects with LFM waveforms,” in Proc. of the 2017 IV Int. Conf. on Engineering and Telecommunication (EnT), pp. 104-107, 2017.
- I. Ostroumov, V. Larin, Y. Averyanova, O. Sushchenko, M. Zaliskyi, and Y. Bezkorovainyi, “Performance analysis of alpha-beta-gamma filter for airplane tracking using automatic dependent surveillance-broadcast,” in Int. Workshop on Advances in Civil Aviation Systems Development, Cham: Springer Nature Switzerland, pp. 60-72, 2023.
- D. Tenne and T. Singh, “Optimal design of α-β-(γ) filters,” in Proc. of the American Control Conference, pp. 4348-4352, 2000.
- T. Jeong, A. W. Njonjo, and B.F. Pan, “A study on the performance comparison of three optimal Alpha-Beta-Gamma filters and Alpha-Beta-Gamma-Eta filter for a high dynamic target,” TransNav, vol. 11, pp. 1-10, 2017.
- B.I. Sighencea, R.I. Stanciu, C. Șorândaru, and C.D. Căleanu, “The Alpha-Beta family of filters to solve the threshold problem: A comparison,” Mathematics, vol. 10, no. 6, 2022.
- J. Khan and K. Kim, “A performance evaluation of the alpha-beta (α-β) filter algorithm with different learning models: DBN, DELM, and SVM,” Applied Sciences, vol. 12, no. 19, 2022.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Electronics, Sensors and Digital Hardware (Other), Control Engineering, Mechatronics and Robotics (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | October 25, 2024 |
| Publication Date | October 29, 2024 |
| Submission Date | August 14, 2024 |
| Acceptance Date | September 17, 2024 |
| Published in Issue | Year 2024 Volume: 6 Issue: 2 |
